POINT cloud has been widely adopted in practical applications such as virtual/augmented reality [1]}, medical 3D reconstruction [2]}, automatic driving [3]}, and video post-production [4]} due to its ability of representing the 3D world. Consequently, plenty of works have been carried out to deal with point cloud classification [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, detection [3]}, and segmentation [13]}, [14]}. However, point cloud quality assessment (PCQA) has gained less attention. PCQA aims to accurately predict the visual quality level of point clouds, which is vital for providing useful guidelines for simplification operations and compression algorithms in applications such as metaverse and virtual/augmented reality (VR/AR) [15]} to not negatively impact users' quality of experience (QoE). Moreover, the point clouds representing vivid objects/humans are usually more complex in geometric structure and contain large amounts of dense points along with color attributes, which makes the PCQA problem very challenging.
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